Python Developer (AI Integration Focus)
Junior(4-7 years)
Senior(8-12 years)
Role Overview
Support development and integration of Gen AI-enabled services, including LLM integrations and emerging agent-based workflows. Work under senior guidance to build scalable APIs and automation components in a cloud-based enterprise environment.
Design and build scalable, enterprise-grade systems integrating GenAI and agentic orchestration frameworks into core business platforms. Lead the development of multi-agent workflows, real-time integrations, and cloud-native architectures, enabling intelligent automation and AI-driven enterprise applications.
Key Responsibilities
- Develop Python-based APIs and backend services
- Integrate LLM APIs into applications
- Design and refine prompts for LLM-based applications
- Support development of simple AI workflows using leading IDEs (VS Code, Cursor, Pycharm)
- Assist in deployment on Azure
- Support integration with core systems and workflow platforms
- Debug, test, and optimize application components
- Maintain documentation and technical specifications
- Experience in using coding agents (GitHub copilot, Cursor etc)
Architecture and Engineering
- Architect and develop Python-based microservices for GenAI and enterprise platforms
- Design and implement cloud-native and serverless architectures (Azure/AWS)
- Build scalable APIs and backend systems for high-performance enterprise environments
- Deploy, manage, and optimize services in cloud environments
Agentic AI & Orchestration
- Design and implement agentic workflows and orchestration layers
- Build multi-agent systems and AI orchestration services
- Implement Agent-to-Agent (A2A) integration patterns
- Design agent registries and service discovery frameworks
- Enable tool-calling frameworks within LLM-driven workflows
RAG & AI Pipelines
- Design, implement, and manage:
- RAG pipelines and architectures
- Embedding workflows
- Real-time document processing pipelines
- Optimize retrieval accuracy and pipeline performance
Enterprise Integration
- Integrate AI systems with enterprise platforms using:
- MCP connectors (Model Context Protocol or equivalent)
- APIs, middleware, and event-driven integrations
- Ensure high scalability, resilience, and fault tolerance
Performance & Operations
- Optimize message handling and high-volume system interactions
- Implement logging, monitoring, and security controls
- Ensure production readiness and operational excellence
Collaboration & Leadership
- Collaborate with business stakeholders, architects, and AI teams
- Mentor junior engineers and guide technical design decisions
Technical Requirements
- Strong fundamentals in Python
- Experience building REST APIs
- Familiarity with:
- FastAPI / Flask / Django
- JSON, async programming basics
- Basic understanding of:
- LLM APIs (Azure OpenAI or equivalent)
- Prompt-based integrations
- Prompt Engineering
- Exposure to:
- Git and CI/CD pipelines
- Azure cloud fundamentals
- Basic database knowledge (SQL / NoSQL)
Core Engineering
- Advanced proficiency in Python
- Strong experience in:
- FastAPI / Django
- Async programming
- Event-driven architectures
- Microservices design
- Experience with:
- Azure/AWS cloud services
- Containerization (Docker, Kubernetes)
- API management / gateway design
AI & Agentic Capabilities
- Strong understanding of:
- LLM ecosystems (Claude, GPT, Gemini)
- LLM integration patterns
- Prompt engineering (few-shot, structured prompting, chaining)
- Tool invocation frameworks
- Experience with:
- Agentic frameworks and orchestration
- Workflow coordination across multiple AI services
- RAG architectures and patterns
- Vector databases
- Familiarity with:
- MCP connectors or contextual integration frameworks
Enterprise Integration
- Experience integrating AI layers with legacy enterprise systems
- Strong understanding of:
- API scalability
- Distributed system resilience
- High-availability architectures
Preferred Qualifications
- Exposure to GenAI projects
- Basic understanding of multi-step AI workflows
- Familiarity with containerization (Docker – basic level)
- Strong communication skills
- Optional: Exposure to insurance domain (Claims / UW) is a plus
- Proven experience implementing agentic AI systems in production
- Strong prompt engineering expertise
- Exposure to Responsible AI frameworks and governance
- Experience working with distributed/global engineering teams
- Strong stakeholder communication skills
- Domain experience: Insurance – Claims / Underwriting systems is a plus
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